1. Field of the Invention
The present invention relates to an apparatus and method for detecting heads in an input image, and more particularly, to an apparatus and method for detecting heads of an input image, which can accurately detect various poses of heads and partially-occluded heads.
2. Description of Related Art
With the evolution of society into a high information society, a customer's desire for an information security system and a customer relationship management system has been recently increased. Accordingly, the need for a more reliable security system for the identification of a corresponding manager and customer security has also increased. Recently, face recognition technology is being spotlighted in various fields, such as security systems, inspection systems, customer relationship management systems in superstores, personal security protection systems, unmanned vending systems, a wide-range communication systems using the Internet, and so on.
Head detection technology has become more important because it is applied to various technology fields such as face recognition technology, a human computer interfaces, video monitoring systems, image searching technology using a face image, and the like. Various face detection algorithms have been recently developed. However, such face detection algorithms are unsuitable to a real life because they have limited detection poses, such as frontal or profile. In the description that follows, the term “pose” refers to human head rotation to some extent in plane and large rotation out of plane; the term “in plane rotation” means the head rotating itself at camera optical axis; and the term “out of plane” rotation means the human turning around with frontal head side, profile head side and rear head side exposed to the camera. For solving such problems, a method for detecting a head by training a decision boundary through a head sample pattern is being studied.
Examples of conventional human body detection technology include “Learning deformable models for tracking human motion” (a PhD thesis of A. M. Baumberg, University of Leeds, 1995), “An unsupervised, online learning framework for moving object detection” (Vinod Nair et al., IEEE Int. Conf. CVPR, p 317-324, June 2004), and the U.S. Pat. No. 6,421,463 disclosing a training system using an support vector machine (SVM) analyzer. The above theses and patents disclose a training system for human body detection, which is also applicable to face detection.
However, the conventional technology fails to consider various changes in a face image according to factors such as illumination, expressions and poses, and cannot accurately detect an occluded face image.